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International Journal of Research in Computer Science 
elSSN 2249-8265 Volume 4 Issue 6 (2015) pp. 9-12 
www.ijorcs.org, A Unit of White Globe Publications 



COMPUTED SCIENCE-TO-ECONOMY 
CONVERSION FOR BETTER FARMING 

AND FORESTRY 

Madhu G Nadig 

Computer Science Department, Kendriya Vidyalaya Hassan, Karnatka, INDIA 
Email: madhug. nadig @ gmaiL com 



Abstract: Forest farming is a three dimensional 
farming technique where the synchronization of 
agriculture and forest environment forms a new kind 
of self-sustaining ecosystem. This method has various 
obstacles inform of versatility and ecological balance, 
which is largely solved by science to economic 
computation and organized structuring of the farm. 
The scientific data regarding the farm - the pH value. 
Rainfall, Humidity, temperatures (day and night) and 
humus content - is processed to get economic results, 
which ultimately helps in practical implementation of 
the method. The main objective is to enhance the 
practicability of the method. 

Keywords: science to economy computation, forest 
farming, economic modeling, computational intelligence. 

I. INTRODUCTION 

Forest farming is the method of cultivation of 
high- value specialty crops under a forest canopy that is 
maintained to provide shade levels and habitat that 
favors growth and enhances production levels. [1] 
Forest farming encompasses a wide range of cultivated 
systems - from introducing plants into the understory 
of a timber stand to modifying forest stands to enhance 
the marketability and sustainable production of 
existing crops. Non-timber forest products are the 
biological materials harvested from within and on the 
edges of natural, manipulated, or disturbed forests. 
Examples of crops are decorative ferns, shiitake 
mushrooms, ginseng, and pine straw. [2] Products 
typically fit into the following categories: edible, 
medicinal and dietary supplements, floral/decorative, 
or specialty wood-based products. [3] 

The commercial success of the method is very 
important in making this form of three dimensional 
farming more versatile. This method can only be 
spread throughout if and only if it brings considerable 
commercial success to the farmer. 



A. Science-To-Economy Computation 

Science to economy Computation is a research 
discipline at the interface between computer science, 
pure science and economics. [4] Areas and subjects 
encompassed include computational modeling of 
economic systems, whether agent-based, general- 
equilibrium, macroeconomic, or rational-expectations; 
through the processing of scientific data. [5] Through a 
directed algorithm, the economic data can be obtained 
by the processing of the scientific input taken using 
computer-based economic modeling for solution of 
analytically and statistically formulated economic 
problems. [6] 

B. Computation in Forest Farming 

The ecological perspective of the forest farming 
coupled with the need for commercial success question 
the practicability of the system. The main problems 
concerning forest farming can be countered by 
employing science-to-economy computation. The 
Scientific input being - the pH value. Rainfall, 
Humidity, temperatures (day and night) and humus 
content while the economic element being the market 
value. 

C. Algorithm for Science to economy computation of 
forest farm: 

i. Scientific input of a particular crop is taken by the 
computational tool. The raw data is transferred to 
the processing block. 

ii. The data is processed and the calculated values 
yield per unit area comes out of the processing 
block. 

iii. The yield per unit area values are multiplied by the 
economic element - the market value, giving out 
the revenue per unit area. 

iv. Total revenue is calculated by multiplying it with 
the associated area. 



Madhu G Nadig 



Input 



Scientific 
Input 



Humus content 
PH 

Temperatures 
Rainfall 



Processing 
Block 



Humidity 




Figure 1: Flow chart of Algorithm 



The processing Block: 

In the processing block, the relative sensitivity of 
the crop towards the five scientific parameters along 
with their ideals values is given numerical 
representation. For convenience - the sum of the 
numerical representations of ideal conditions of 
individual parameter is made 100. For different values 
of these parameters, numbers are assigned. The 
database contains raw data regarding the numbers to 
be assigned for a given value of a parameter as well as 
the yield per area values for a given sum total. 



When the input comes into the processing block, 
the scientific values are separately compared with 
values in the database and are assigned a number. 
After the comparison and number assignment, all these 
numbers are added up to obtain the sum total. Thus for 
ideal conditions for a plant, this sum is equal to 100. 
The resultant sum is again compared to the yield per 
area values in the database and the calculated value for 
yield per area comes out the processing block 



Comparision and Number Assignment 



Scientific 
Input 




Humus content ) 

pH \ 



Temperatures 
Rainfall 

Humidity 




Yield Per 
Area 



Figure 2: Pictorial Representation of Process Block 



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Computed Science-to-Economy Conversion for Better Farming and Forestry 



11 



II. IMPLEMENTATION 

The implementation is done in the form of growth 
of multiple crops in the same area, computing the 
results from the observed scientific data and 
preparation for the yield based on the computed result. 
The land is distributed among several inter-linked 
crops which form a loosely bound eco system of their 
own. This eco system makes the farm self-sustainable, 
thus not needing 



Additional man made efforts to maintain the farm. 
The diversity also helps in financial success and makes 
it less prone to complete meltdown 

The Experiment: 

The method is tried in one acre of land in Hassan - 
Sakaleshpur agricultural region. The observations were 
carried out with a span of three months from April 
2010. The farm includes as many as 12 species of 
flora divided as follows: 



Papaya 
3%Pond^° 

Houses 

3% 
Spinach. 

3% 

Mango, 
and Bitter 
gourd 
5% 

Melia dubia 



Ruta 

chalpensis Area wise distribution of the land 

Garlic^ 2% 

0/ ^sapota 




ack fruit 
vanilla 
6% 

Figure 3: Area wise distribution of land 
else 



The prototype computational tool was programmed in 
C++. Code snippet used for comparison and number 
assignment-from the prototype without the use of 
database: 

Forest f 1 ; 

Fl.val_pH=0; 
Cout«"Enter pH: \n"; 
Cin»float p; 

if(p=<7&&p>6.8) 

{ 

Fl.val_pH=20; 

} 

else if(p=<6.8&&p>6.3llp=<7.5 &&p >7) 
{ 

Fl.val_pH=15; 

} 

else if(p=<6.3llp>6llp=<7.8&&p >7.5) 
{ 

Fl.val_pH=10; 

} 



Fl.val_pH=0; 



} 



DOSftci 0.74. C pu speed: 5iKM c^ clci, Framcskip Pwjijrflm: DOSS OX 




Figure 4: Main Input menu of the computational Tool 



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12 



Madhu G Nadig 



III. RESULT 
Land Available For Farming- 1 Acre 

Crop grown - Paddy 

Overview of 2012-2013: 

Table 1: Comparison of Forest Faming yield with Organic 
Farming yield. 



Table 2: Accuracy in Economic Predictions 



Table 3: Accuracy in Yield Predictions 



the basic aim of forest farming. Further research and 
development encouraged in this domain. 



[1] 



YIELD OBTAINED (IN TONS) 


MONEY 
OBTAINED 


1.1 


46000 ? 


YIELD OBTAINED (IN TONS) 
Complete Organic Farming 


MONEY 
OBTAINED 


0.9 


41000 ? 



[2] 



CASH PREDICTED 


CASH 
OBTAINED 


ACCURACY 


50000 ^ 


46000 ? 


92% 



[3] 



[4] 



[5] 



YIELD 


YEILD 


ACCURACY 


[6] 


OBTAINED 


PREDICTED 






1.1 


1.2 


84.4% 










[7] 



IV. CONCLUSION 



V. REFERENCES 

Chamberlain, J.L.; D. Mitchell, T. Brigham, and T. 
Hobby (2009). "Forest Farming Practices". North 
American Agroforestry: an integrated science and 
practice (2nd ed.). Madison, Wisconsin: American 
Society of Agronomy, pp. 219-254. 

Chamberlain, J.L.; D. Mitchell, T. Brigham, and T. 
Hobby (2009). "Forest Farming Practices". North 
American Agroforestry: an integrated science and 
practice (2nd ed.). Madison, Wisconsin: American 
Society of Agronomy, pp. 219-254. 

Vaughan, R. C; J. F. Munsell, and J. L. Chamberlain 
(2013). "Opportunities for Enhancing Nontimber Forest 
Products Management in the United States.". Journal of 
Forestry 111 (1): 26-33. 

Computational Economics. ""About This 
Journal" and "Aims and Scope." 

Scott E. Page, 2008. "agent-based models," The New 
Palgrave Dictionary of Economics, 2nd Edition. 
Abstract. 

The New Palgrave Dictionary of Economics, 2008. 2nd 
Edition "computation of general equilibria" by Herbert 
E. Scarf. Abstract. 

Douglas, J.S.; R. A. de J. Hart (1984). Forest farming: 
towards a solution to problems of world hunger and 
conservation. London: Intermediate Technology 
Publications. 



This method's results have been satisfactory hence 
it is concluded that it can be practically implemented 
to a larger area. More importantly, unlike alternative or 
conventional methods, this eco-friendly farming brings 
gains to farmer, so it can broadly be executed without 
troubling the economy; farmers will be more inclined 
to use this method. Thus it is more practicable form of 
organic farming and is superior to other methods like 
mixed cropping. Economic modeling also solves all 
the related problems such as reaction to crop yield 
fluctuation; thus creating a whole new field of 
computational simulations. The synchronization could 
only work if the conditions were found suitable for the 
introduced species; this problem is not associated with 
mixed farming. The interconnection of various species 
built a new self-sustaining ecosystem, thus fulfilling 



/ \ 

How to cite 

Madhu G Nadig "Computed Science-to-Economy Conversion for Better Farming and Forestry". International Journal 
of Research in Computer Science, 4 (6): pp. 9-12, June 2015.